Data Management in an Operational Context: A study at Volvo Group Trucks Operations

Data Management in an

Operational Context

A study at Volvo Group Trucks Operations

Author: Martin O. Enofe

Supervisor: Azadeh Sarkheyli

Examiners: Olgerta Tona Paul Pierce

Data Management in an Operational Context: A

study at Volvo Group Trucks Operations

Author: Martin O. Enofe

Publisher: Dept. of Informatics, Lund University School of Economics and Management.

Document: Master Thesis

Number of pages: 66

Keywords: Data, Information, Data Management, Industry 4.0, Future manufacturing

Abstract:

The present customer based and data driven world of business, together with the evolving social and technological trends continue to drive product complexity and product variety to a new height, one that is characterized by product customization. In the product realization pro-cess, the assembly floor remains the most vital part of the manufacturing process; to cope with the rigorous demands of product complexity and product variety, operators rely heavily on quality assembly instruction in manual assembly operations. Previous studies conducted at Volvo Group Trucks Operations (GTO) revealed the different working processes for creating assembly instructions, due to historical growth and acquisitions of other truck manufacturers. These different working processes are one of the main reasons for the reported quality devia-tion in assembly operadevia-tions. This thesis therefore provides the current state analysis of data and information handling in the product realization process. Two research methods, direct observation and Semi-structured interview, were employed to investigate the availability and usage of data and information in the engineering phase and in the assembly phase. The study was conducted at manual assembly stations across three production plants within a case com-pany. The result indicates gaps and differences in opinion of how data is perceived, interpret-ed and utilizinterpret-ed. Hence there are no standard approach on how data and information is man-aged in the manufacturing engineering IT systems and assembly information systems within all levels of the organization.

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Acknowledgements

In the history of mankind, no one is known to have succeeded without the contribution of people either actively or passively. Hence, without the encouragement, support and dedication from the following people, this thesis, which was carried out between October 2016 and May 2017 in collaboration between Lund University and Volvo Group Trucks Operations, would not have been a success.

§ First, my supervisor at Volvo GTO, Pierre Johansson and Lena Moestam. Thank you for your unprecedented support and guidance during the course of this study and for offering me the opportunity to write my master thesis with a global company like Vol-vo GTO.

§ My supervisor at Lund University, Azadeh Sarkheyli. You deserve a special thanks for always challenging me throughout the course of this thesis.

§ My colleague/ team, Moritz Schwarzkop, Johan Sahlström, Thomas Lezama, Frida Schildauer, Hanna Atieh, Wang Zhiping, Pontus Johansson and Gustaf Eriksson. Thank you all for the enjoyable moments together.

§ The Operators and Engineers, who participated during observation and interview sec-tions. You guyz were always willing and cooperative. My sincere gratitude.

§ My friends, you have always been with me in good and bad times. My deepest appre-ciation.

§ Last but not least, my family and love ones. You are the most wonderful; for without you my life would have been empty. I love you to the bonez.

© Gothenburg/ Lund, May 2017

Dedicated to the loving memory of my father Mathias O. Enofe; you are always on

my mind…. never to be forgotten.

Reminisce your legacy is what I owe you…. you laid the foundation for this work; I

am just building on it.

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Content

2.1 Data and Information overview ... 15

2.2 Data Management ... 17

2.2.1 Importance of Data Management ... 17

2.2.2 Relationship between Data Management and Information Management ... 17

2.2.3 Relationship between Data Management and Enterprise Architecture ... 18

2.3 Product Data Management ... 20

2.4 Data and Information Management in Future manufacturing ... 20

2.4.1 Industry 4.0 ... 21

2.4.2 Data Management and Industry 4.0 ... 22

3 Methodology ... 24 3.1 Research strategy ... 24 3.2 Research design ... 24 3.3 Research approach ... 25 3.3.1 Qualitative data ... 26 3.3.2 Quantitative data ... 26

3.4 Data collection method ... 26

3.4.1 Literature review ... 26

3.4.2 Observation ... 27

3.4.3 Semi-structured interview ... 27

3.5 Data analysis method ... 28

3.6 Research quality criteria ... 28

3.6.1 Reliability ... 29

3.6.2 Validity ... 29

3.6.3 Ethics ... 29

4 Empirical data ... 31

4.1 Company profile ... 31

4.2 Volvo GTO Information systems ... 32

4.3 Process and preparation of assembly instruction ... 33

5 Data analysis and results ... 35

5.1 Availability and used data for assembly operations ... 35

5.2 Importance of data for assembly operations ... 38

5.3 Information model and data mapping ... 40

6 Discussion ... 46

6.1 Current situation ... 46

6.2 Data management in operational context ... 48

6.3 Chosen methodology ... 48

7 Conclusions ... 49

7.1 Relevance of the study ... 50

7.2 Recommendations ... 50

7.3 Suggestion for future study/ research ... 51

Appendix A: Assembly Instruction samples ... 52

Appendix B: Data matrix of selected assembly stations ... 54

Appendix C: Observation questionnaire ... 57

Appendix D: Interview transcript ... 59

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Figures

Figure 1.1 Thesis disposition ... 14

Figure 2.1 Data, Information, Knowledge, Wisdom (DIKW) model ... 15

Figure 2.2 Relationship between Data and Information ... 16

Figure 2.3 Connections between Data and Information Management ... 18

Figure 2.4 Enterprise Architecture framework ... 19

Figure 2.5 Data Management in diverse systems ... 20

Figure 2.6 Evolution of the manufacturing paradigm ... 22

Figure 3.1 Different research approaches ... 25

Figure 5.1 Information model of Assembly Instruction process in Plant A ... 41

Figure 5.2 Information model of Assembly Instruction process in Plant B, and C ... 43

Tables

Table 3.1 Summary of research methodology used ... 30 Table 5.1 Data availability and usage for assembly operation at respective plants ... 36 Table 5.2 Importance of data for assembly operation at respective production plants ... 39

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List of abbreviations

DM - Data Management

AI - Assembly Instruction

IT - Information Technology

IoT - Internet of Things

CPS - Cyber Physical System

GTO - Group Trucks Operations

CAD - Computer Aided Design

PDM - Product Data Management

AVG - Automatic Guided Vehicle

BoM - Bill of Material

DCN - Design Change Note

ICT - Information Communication Technology

ECN - Engineering Change Notice

GPN - Global Production Network

IIoT - Industrial Internet of Things

DAMA - Data Management Association

EDM - Engineering Data Management

CAM - Computer Aided Manufacturing

GAIS - Global Assembly Instruction Strategy

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1

Introduction

In this thesis, issues concerning data and information management (based on the initiative of industry 4.0 “smart factory”) within manufacturing assembly information systems are dis-cussed. This chapter will introduce the reader to the background of the study and research area, together with the problem definition that has been identified with respect to the specified area. The identified problem will further be discussed scientifically and formulated into re-search questions, which will be the bases of the study. The purpose of the study, relevance and delimitations will also be defined and presented. Lastly, an overview of the thesis struc-ture is shown accordingly.

"Organizations that do not understand the overwhelming importance of managing data and information as tangible assets in the new economy will not survive."

Tom Peters, 2001 (Mosley et al., 2009, pp. 1)

1.1

Background

In an ever-evolving global world, Information Technology (IT) and Information Communica-tion Technologies (ICT) has advanced rapidly; facilitating the way people, organizaCommunica-tions/ businesses communicates and connects with each other (ITU, 2015). The technological revo-lution is virtually transforming everything, from everyday living, to businesses, and even the way data and information is being collected, managed and conveyed, and utilized (Lechman, 2015). Global development has also been driven immensely by ICT revolution, cutting across every businesses and organization, creating an innovative platform for competition, character-ized by an increasing concession for information, knowledge, agility and connectivity (Hanna, 2010). To quote Hanna (2010) ICT revolution “is so profound and pervasive that it challenges many traditional economic concepts that are rooted in incremental thinking. (….) The infor-mation and communication revolution – perhaps the most pervasive and global technological revolution in recent human history” (Hanna, 2010)

“ICTs encompass all those technologies that enable the handling of information and facili-tate different forms of communication among human actors, between human beings and electronic systems, and among electronic systems. These technologies can be sub-divided

into: capturing technologies, storage technologies, processing technologies, communica-tion technologies and display technologies” (Hamelink, 1997, No 86, pp. 3).

As technology development continues to experience an unprecedented growth, manufacturing companies are continually faced with the challenging needs to optimize production and in-formation processes (Vollmann et al., 2005) due to the ever increasing competition in the market, economy and global development, and the need to keep market shares (Lee et al., 2014). These new and complex technologies have led an increasing numbers of varieties of manufacturing standards that do not only affect the industry or market but also innovation and technology diffusion (Tassy, 2000).

In the present technology-based and data driven world of business, data have swiftly become the raw material of production, an innovative source for enormous socio-economic value (Tene and Polonetsky, 2013). The flow of data and information between databases, infor-mation systems, processes etc., conveys the capability that can make an organization to be cost effective, efficient and more importantly competitive (The MITRE Corporation, 2016). Consequently, effective data management throughout its lifecycle provides a foundation not just for effective and robust processes but also for a competitive edge in the global market (Lee et al., 2014; The MITRE Corporation, 2016). This market is connected electronically and also dynamic in nature. Thus, companies try to improve their level of agility with an objective of being flexible and responsive to meet customers and changing markets requirements (Chandrasekhar and Ngai, 2003). In an effort to protect market position, expand customer base and gain market shares, companies have started offering diversified product in the form of mass customization to meet the need of customers (Johansson et al., 2016). Mass customi-zation is a manufacturing strategy which aims to deliver customized products/ services at a reasonably low cost, based on the requirements and specification of individual customer (Co-letti and Aichner, 2011) a strategy that is regarded by large number of companies as a means to attain competitive advantage (Da Silveira et al., 2001).

1.2

Problem definition

Social economic activities have been broadened globally due to technological development (Chandrasekhar and Ghosh, 2001). This rapid development and recent advancement of mod-ern ICT can be considered as drivers for globalization (Johansson et al., 2015). This has given companies the platform and opportunities to grow rapidly on the global marketplace (Johans-son et al., 2015) and also the possibilities to acquire other manufacturers, in a view to be clos-er to the markets (Delin and Jansson, 2015). According to the research carried out by Delin and Jansson, (2015), companies have started to broaden its business through the acquisition of other companies and off-shore production, which however has led to variances in company’s operational activities. To quote Johansson et al. (2015) “globalization is one of the reasons companies have hard times creating global standards when product types and brands histori-cally have been different”.

Presently, one of the top priorities for virtually every company is to improve operational com-petitive advantage (Manrodt and Vitasek, 2004) this is fundamental because gaining competi-tive advantage is perceived as increasingly significant, giving emphasis to new product crea-tion and the challenging need to react to changing markets (Fowler, 1995). Fundamentally, a means to attain and sustain competitive advantage is through internal process management and global standardization (Manrodt and Vitasek, 2004) one of the reasons for this is that companies now find it difficult to compete solely on products functionality and cost alone (Fowler, 1995). According to Johansson et al. (2016) companies have also broadened their

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customer base through globalization and access to new markets by the introduction of prod-ucts that are customized according to the requirements of each customer, otherwise known as mass customization.

Volvo Group Trucks Operations (GTO) currently offers customized product, in form of mass customization. Mass customization is Volvo GTO core business strategy, and through devel-opment and acquisitions of other manufacturers over the years, Volvo Group now offers a wide range of other trucks brands (e.g. Renault trucks, Mack trucks, UD trucks, Eicher, etc.) produced in 43 different plants around the world, with the philosophy of offering highly cus-tomized trucks according to the need of its customers. This acquisition strategy has led to an expansion of the customer base and penetration into new markets. However, as these devel-opments (growth in customer base and new markets penetration), and product customization continue to increase, the complexity of the manufacturing process also intensifies. These have led to a mix of working processes in the manufacturing and execution process of these varie-ties of products offered by Volvo GTO.

Volvo Group Trucks faces challenges on the implementation of global industrial systems due to acquisition of different manufacturers in recent years (Johansson, 2016). This historical growth has led to diversity and development of different manufacturing and assembly process with the different brands of the Volvo GTO (Delin and Jansson, 2015). As part of GAIS - Global Assembly Instruction Strategy II project (a continuation of GAIS1 project) with the aim to standardize manufacturing preparation process of assembly instruction across global operations, a study was conducted by Johansson et al. (2015) on the use of data and infor-mation in manual assembly instructions of heavy vehicles. This study is a continuation of the national study conducted during the spring of 2014, within the Global Production Network (GPN). The study indicates that diversity exists in how assembly instructions are created, and this diversity not only exists within the GPN, but also within production sites. Thus, there is need for the implementation of global standard or strategies for assembly work instruction due to different approaches to manufacturing preparation of assembly instruction in different pro-duction sites, different systems being utilized in the process, and variation in execution of assembly operations. To create these standards according to the initiatives of the future indus-try for example indusindus-try 4.0, current use of data and information in the product realization processes of manual assembly need to be studied.

1.3

Research questions

Based on the nature and scope of this study, the research was approached from the perspective of data and information, which is available, used and important to the operator for the assem-bly operation, along with the information flow in the product realization processes. Therefore, the research questions for the thesis are;

RQ1 - How is data and information currently utilized in the manufacturing prep-aration process and the execution of manual assembly operation?

RQ2 - What is the significance of data management in manufacturing opera-tions?

1.4

Purpose

This research is carried out in an academic and an industrial context; thus, the purpose of this thesis is to empirically explore how data and information is used in the manufacturing prepa-ration IT systems and assembly information systems for manual assembly opeprepa-rations, so as to outline design requirements for imminent information systems for manual assembly opera-tions.

The findings of this research will strive to contribute to the process of standardization for fu-ture assembly information systems across global operations. More so, ascertain the signifi-cance of data management in an operational context.

1.5

Delimitation

Data and information management is primarily discussed throughout the study. Data in this thesis is defined as facts, figures or symbols, while information represent data that has been interpreted and processed to be useful in order to provide meaning (Bellinger et al., 2004). In order for information to be created or to happen, data must first exist. Thus, “data is the pre-requisite for the creation of information and knowledge” (Kans, 2008). Although, data and information is often used interchangeably in most studies as it will also be used in this thesis, it is however worthwhile to state the distinctive difference between the terms. This study therefore focuses on data and information handling in the assembly information systems for the preparation of manual assembly instructions within Volvo GTO production plants (re-sponsible for the manufacturing of heavy duty vehicle and heavy duty vehicle components) in Sweden. More so, the empirical data for this study was gathered within these plants. There-fore, other functions and operations within the production process will not be considered, and thus, delimited, as emphasis is only on the preparation of manual assembly instruction and processes.

1.6

Thesis disposition

The outline of the thesis comprises of six chapters, with the research questions used as a con-necting thread within the study. Each chapter gives a brief description of the content, thus, aiding the reader’s understanding of what to expect in each chapter. Figure 1.1 shows the structure and disposition of this thesis.

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2

Literature review

The literature review provides a strong knowledge base for this research. This chapter aims to present the theoretical background to help the reader understand the context of the thesis. It introduces the concepts of Data and Information Management in manufacturing operations, as well as an overview of data management in the industry of the future (industry 4.0) using pre-vious research articles and literature.

2.1

Data and Information overview

According to data is recognized as an indispensable enterprise asset in the modern infor-mation era, thus data and inforinfor-mation are the lifeblood of the contemporary economy. How-ever, data and information are easily confused and are often used interchangeably (depending on the context) but there is a subtle difference between the two. It is therefore important to look at them separately as an entity, and understand the difference and relationship between the two. There are many definitions of data in dictionaries, articles or textbooks, which basi-cally refers data as “a body of facts or an item of information”. According to the Data Man-agement Association (DAMA) data is “a representation of facts as text, numbers, graphics, images, sounds or video” (Mosley et al., 2009).

Data, Information, Knowledge, and Wisdom are closely related, from the perspective of the DIKW model (as seen in Figure 2.1). However, this thesis focuses on the management of data and information, hence; emphasis will be on data and information.

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Information on the other hand is data that has been put in context, i.e. information is created when data is processed or interpreted in order to give meaningful or useful information. Data is therefore the foundation of the DIKW model, which stands for Data, Information, Knowledge, Wisdom, and describes the hierarchy between them (see Figure 2.1.) the begin-ning of a continuum – from data, to information, then to knowledge, and ultimately to wis-dom, which is the shared understanding (Mosley et al., 2009; Gordon, 2007).

“This principle of sharing information and building on discoveries can best be understood by examining how humans process data. From bottom to top, the pyramid layers include data, information, knowledge, and wisdom. Data is the raw material that is processed into infor-mation. Individual data by itself is not very useful, but volumes of it can identify trends and patterns. This and other sources of information come together to form knowledge. In the sim-plest sense, knowledge is information of which someone is aware. Wisdom is then born from

knowledge plus experience. While knowledge changes over time, wisdom is timeless, and it all begins with the acquisition of data” (Evans, 2011, pp. 6).

Data according to (Mosley et al, 2009) is the raw material, which when interpreted or pro-cessed, constantly creates information in various forms that guides or is used for decision-making. Thus, information is an essential business asset used basically in every areas and lev-els of business or enterprise, because it supports daily operations and practices, whether ad-ministrative, management or making strategic decisions (Gordon, 2007). From the context of a computerized information system, the relationship between data and information is shown in Figure 2.2.

Figure 2.2 Relationship between Data and Information (Gordon, 2007)

The needed information is extracted by the system user, based on knowledge of specific phe-nomenon, and put in the system. The information is converted into data in order to be stored

and processed. When information is desired by another user, the data is interpreted so that it is meaningful and useful to the user, according to the context and needs (Gordon, 2007)

2.2

Data Management

Data management (DM) is a very broad scope covering different functional areas. According to DAMA, data management is “the development of, execution and supervision of plans, pol-icies, programs and practices that control, protect, deliver and enhance the value of data and information assets” (Mosley et al, 2009).

In today’s business environment, an essential and distinctive feature of any organization or business is increased emphasis on data and its management. DM is as important to an organi-zation as the management of other resources, and encompasses all activities that are related with administering and controlling how an organization use data internally, together with planning and implementing the processes and systems used by the organization to perform or accomplish these tasks (Fowler, 1995). In the same vein, Gordon (2007) stated that data man-agement is a corporate service that facilitates the delivery of information services through control or organization of the classifications and utilization of reliable and relevant data as it is a broad function that is characterized by data definition to ensure sharing between infor-mation systems and thus become a corporate resources (Gordon, 2007).

2.2.1 Importance of Data Management

The essentials and benefit of DM cannot be overemphasized as it occurs at virtually every levels of an organization. It can be argued that computer systems basically provide rudimen-tary capabilities for systematic data structure; however, it is of essential needs to employ data management approaches when such systems are shared between people and business units (Fowler, 1995).

One key benefit of DM (as related to business) is the value-added information availability obtained through data sharing between the distinct IT systems, as well as improved data quali-ty (Gordon, 2007) which leads to improved efficiency in organization operations and competi-tive advantage. Other benefit of data management (as related to information technology and systems) is an upturn productivity in system development and cost saving, through the reuse of information and data analysis products (Gordon, 2007).

2.2.2 Relationship between Data Management and Information Management

Data administrative, database administrative and repository administration are three distinc-tive roles within the DM function. The relationship between these three roles and data man-agement, together with the wider concept of information management is shown in Figure 2.3.

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Figure 2.3 Connections between Data and Information Management (Gordon, 2007)

However, other roles from the perspective of information management, which are not within the scope of data management, are process management, system management, and business information management (Gordon, 2007). Accordingly, Information management among oth-ers (e.g. Enterprise data management, Enterprise information management, Data resources management, etc.) is generally synonymous with DM (Mosley et al, 2009) due to their close relationship, thus it is almost impossible to mention one without the other.

Evidently, data management happens in many levels of an organization, thus sustaining con-trol of data requires that it is concon-trolled or structured in a systematic way (Fowler, 1995). As stated by DAMA, DM is the business function which involves the planning for, controlling and delivering data and information assets, taking into account the discipline of development and supervision, policies and procedures which control and enhance data and the importance of information resources (Mosley et al., 2009).

2.2.3 Relationship between Data Management and Enterprise Architecture

As Gordon (2007) puts it, if any organization that is adequately perceptive to implement DM, there is a good chance that such organization has embraced the concept of enterprise architec-ture. Enterprise architecture involves the understanding and perceptiveness of different ele-ments the enterprise is made of. This element includes people, information, processes, com-munication, and most significantly, how these elements interrelate. Thus, the implementation of enterprise architecture is of fundamental importance in order to define how these elements collaborate to meet organization business needs and strategic goals (Gordon, 2007). The En-terprise Architecture in relation to data management and operations, according to Zachman (2008) is shown in Figure 2.4.

Figure 2.4 Enterprise Architecture framework (Zachman, 2008)

The Enterprise Architecture is divided into areas from different perspective of an organiza-tion. The first five rows of the Enterprise Architecture are;

1. The Executive perspective - view of the business context planners with models for each column that document the scope of the enterprise

2. The Business management perspective - view of the business concept owner with models for each column that document the business concepts within the enterprise (business definition models)

3. The Architecture perspective - view of the business logic designers with models for each column that documents the system logic within the enterprise the system repre-sentation models

4. The Engineer perspective - view of the business physics builders with models for each column that document the technology of the enterprise, the technology specifica-tion models.

5. The Technician perspective - view of the business component implementers with models for each column that document the tools of the enterprise, the tool configura-tion models (Gordon, 2007).

The sixth row, which is the Enterprise perspective, represents the functioning enterprise of the framework. The framework for Enterprise architecture involves data, information, and/ or data and information specification at different levels of an organization. Consequently, close collaboration and co-operation between data management function and enterprise architecture development owner is crucial (Gordon, 2007).

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2.3

Product Data Management

Product Data Management (PDM) involves both the processes, which are used to manage the product data and the computer systems that are used to implement and execute these process-es (Fowler, 1995). Another widely used term within the manufacturing field is the Engineer-ing Data Management (EDM), which is the use of autonomous database systems to manage data across multiple, heterogeneous design and engineering application (Fowler, 1995) these systems provide a level of governance over product definition data that is impossible from Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) application (Fowler, 1995). Figure 2.5 depicts this relationship and management of data between different systems.

Figure 2.5 Data Management in diverse systems (Fowler, 1995)

PDM system is used as an enabler to define and manage the relationship between CAD sys-tems (which may be used to create different illustrations properties of a product), in terms of a product in which they relate, as well as the fulfillment of the requirement for the logical iden-tification of product data elements within a heterogeneous system and the physical location of those data elements within one or more systems (Fowler, 1995).

2.4

Data and Information Management in Future manufacturing

As industries continue to move in accordance with the initiatives of industry 4.0, data is in-creasingly being used to make strategic decision in real-time, as well as also revealing new

trend in customer behavior (Igor et al., 2016) whereby customers are becoming increasingly influential in product development leading to a new trend in manufacturing. Manufacturing industries are currently facing a growing amount of data and this trend is bound to continue because of the continuous advancement of digitalization of business processes, data output, among others (Stich, 2015). Manufacturing is entering new data-driven era, an era wherein data will most definitely play a significant role at all levels (Sill, 2016).

The fourth industrial revolution, which is touted as industry 4.0 “Smart factory” is about ex-ploiting the overwhelming power of data (big data, predictive analytics, artificial intelli-gence), and also includes smart manufacturing (Waycott, 2016). According to the Smart man-ufacturing Leadership Coalition (SMLC), Smart manman-ufacturing is “the ability to solve exist-ing and future problems via an open infrastructure that allows solutions to be implemented at the speed of business while creating advantaged value.” It is a data driven paradigm (a future state of manufacturing) that advocates real-time data or information communication and shar-ing across ubiquitous networks, with the sole objective to create manufacturshar-ing intelligence across all aspect of the factory (O’Donovan et al., 2015). The authors argued that the objec-tive of both smart manufacturing and traditional manufacturing and business intelligence is quite comparable, in the sense that it focuses on the transformation of raw data to knowledge. They however stated that given the excessive focus of smart manufacturing on real-time col-lection, aggregation and sharing of knowledge across physical and computational processes to produce an efficient continuous operating intelligence stream, makes it delineate from tradi-tional manufacturing intelligence (O’Donovan et al., 2015). Smart manufacturing pursue the extension of manufacturing techniques to include autonomous, adaptive processes and to in-corporate these processes into modern information technologies (Sill, 2016) thus, manufactur-ing can be seen as an intensified manufacturmanufactur-ing intelligence application in which every areas of the factory is monitored, optimized and visualized (O’Donovan et al., 2015).

2.4.1 Industry 4.0

Widely regarded as the fourth industrial revolution and the factory of the future, industry 4.0 is an advanced information and communication technology (ICT) enabled industry “smart factory” of intelligent and interconnected production systems driven by for core elements, the Cloud technology, internet of Thing (IoT), Intelligent Machines, and Big Data (Khan and Turowski, 2016). According to Kagermann et al. (2013) Industry 4.0 refers to “the technolog-ical evolution from embedded systems to cyber-phystechnolog-ical systems” representing a paradigm shift from a centralized production to decentralized production (Kagermann et al., 2013). The goal of industry 4.0 according to Jazdi, (2014) is the development of a digital factory, which is characterized by smart networking, mobility, flexibility, customer integration, and new in-novative business models.

According to Kagermann et al. (2013), manufacturing and the world economy since the end of the 18th _{century has been shaped by three major technological revolutions. The first} indus-trial revolution was the introduction of water and steam-powered mechanical manufacturing facilities. The second period was the beginning of electrically powered industrial mass pro-duction based on the division of labor. The third industrial revolution was the intropro-duction of electronics and IT in industrial process, paving the way for automation in manufacturing. To-day, we are experiencing the fourth industrial revolution (based on Cyber-Physical Systems) that promises to connect production systems and network connectivity in an IoT, one which is characterized by the integration of intelligent ICT into manufacturing systems (Kagermann et

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al., 2013). Industry 4.0 connects embedded systems production technologies and smart pro-duction processes to pave the way for an innovative technological era (Kagermann et al., 2013) thus the future of industrial manufacturing will be famed by the importance of product customization through augmented mass customization (Hu et al., 2011). Figure 2.6 shows the industrial manufacturing evolutions since the end of 18th _century.

Figure 2.6 Evolution of the manufacturing paradigm (Hu et al., 2011)

Manufacturing evolution or industrialization began in the 18th _{century, where craft production} was prevalent. Under craft production era, individual products (with details to customer in-formation) were produced at a very high production costs. This high costs associated with individual products, led to an era of mass production in the 19th century, whereby little or no customers details was needed to produce a product. However, from early 1980’s, industries started adopting mass customization. Mass customization enables product to be produced ac-cording to customer specification and requirement, and at an equitable cost.

The future of manufacturing characterized on product complexity and personalized products, as shown in figure 2.6. Thus; data and information mining (Data Management) will play a significant role, due to the connection between the customer product requirements and manu-facturing environment (Hu et al., 2011).

2.4.2 Data Management and Industry 4.0

There have been increased applications of advance technologies on production processes due to increased product complexity, new trend in consumer behavior (with regards to product requirements), and the competitive pressure in the dynamic global economy (Khan and Turowski, 2016). These new trends have been made even more visible since the advent of big

data and analytics. In real-time, smart manufacturing together with big data has propelled vis-ibility to whole new level. Hence, with the visible real operational environment, production, supervisors, and operators can make improved and more informed decisions. Thus smart manufacturing is more about collecting data, processing and managing it to make meaningful information and knowledgeable decisions (Waycott, 2016). To quote Pat Gelsinger (Chief Executive Office, VMware Inc.) “Data is the new science. Big data hold the answer”.

Data management in Industry 4.0 focuses on the application of interconnected systems, oth-erwise known as Cyber-Physical System (CPS) with Data Acquisition and Big Data Analytics as the core elements (Lee et al., 2014). Presently, the business world is data-driven, and large amount of data is being generated and collected daily in manufacturing environment (for ex-ample, shop floor). Data from machines, sensors, processes, product, quality, maintenance, etc., all contributes to the increase of the amount of data being generated (Khan and Turowski, 2016). This explosion of the amount of data will continue to increase as the devel-opment and rapid use of Industrial Internet of Things (IIoT) devices increases. Peter Hartwell, a senior researcher at HP Labs: stated "With a trillion sensors embedded in the environment, all connected by computing systems, software and services, it will be possible to hear the heartbeat of the Earth, impacting human interaction with the globe as profoundly as the Inter-net has revolutionized communication". Cisco IBSG predicted that there would be 50 billion Internet connected devices (IoT) by the year 2020 (Evans, 2011) opening new windows of opportunities and new use cases in every sector (Khan and Turowski, 2016).

Internet of Things (IoT) or IIoT integrates machine learning and big data by harnessing the power of sensor data and automation (Waycott, 2016). According to Evans, (2011) the amount of raw data that is available for processing has dramatically been increased by the emergence and development of IoT, together with the ability of the Internet to communicate this data, will consequently enable and ease the advancement of people even further. Due to the fact that, the more data is created or generated, the more obtainable is knowledge and wis-dom (Evans, 2011). Data management and analytics will play a significant role because big data will lead the way (i.e. inform people and organization on what to do) for effective deci-sion that will affect manufacturing efficiency and improved performance.

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3

Methodology

The methodological approach and the research design of this study are discussed in this chap-ter. The research strategy was first discussed, followed by the data collection techniques and data analysis process with emphasis and justification as to why the author has adopted the research method or approach. Finally, an explanation of how the research quality and ethics was ensured is discussed.

3.1

Research strategy

A well-crafted and appropriate research strategy should be an enabler for answering a re-search question or questions, as well as attaining the rere-search goal. Rere-search strategy there-fore is characterized by a set of pre-defined steps to approach a problem so as to fulfil the purpose of the study (Saunders et al., 2007). Three main conditions (i.e. Form of research questions, control of behavioral events, and focus on contemporary events), which are associ-ated with different research strategies (e.g. Experimental, field survey, archival/ secondary data analysis, history, and case research) as stated by Yin (2009), will assist in the evaluation of the kind of strategy that is most appropriate in conducting a research. As a result, case re-search strategy is a more suitable rere-search strategy for this study because the studied phenom-enon in this research is a description of a continuous process of the Volvo Group Operations. Besides, it is an effective strategy to explore “how” and “what” questions (Yin, 2009). Refer-ring to Voss et al. (2002), case research is a method that utilizes cases studies as its basis by enabling the phenomenon to be studied in its natural settings from the understanding gained through observation of the actual activities, and allowing the “why”, “how” and “what” ques-tions to be answered with relatively full understanding nature and complexity of the phenom-enon. The research questions in this study aims to examine a phenomenon, thus, adopting a case research strategy will allow a holistic understanding of the complexity of data and infor-mation management within manufacturing preparation and assembly processes, which are inseparable from an operational context (Yin, 1989).

3.2

Research design

According to Bhattacherjee (2012) the nature of a research is largely determined by the pur-pose of the study. This thesis research questions and purpur-pose clearly indicates a descriptive form of research design because the study will not only strive to increase the understanding of the use of data and information in the manufacturing process and IT systems for assembly work instruction, but also attempt to find out the significance about data and information management within the studied object. This corresponds with Dhawa (2010) that it is essential for researchers to clearly describe a phenomenon under evaluation, and the population sample under investigation, when carrying out a research.

Generally, a research design is characterized by three different forms; exploratory, descrip-tive, and exploratory (Robson, 2002), which enables a well-defined research design,

depend-ing on the research area and its purpose (Bryman and Bell, 2007). Descriptive research design is more appropriate and most applicable for this kind of research because of its purpose to empirically increase the knowledge base. Descriptive research design focuses on finding facts or result about a particular subject or object of study that is not clearly defined (Yin, 2003) and answers the question (in the form of “what”, “how”, “why”, etc.) as clearly stated with the research questions.

3.3

Research approach

The research approach to a study is usually directed by a well-formulated research question (Bhattacherjee, 2012). This thesis therefore takes a deductive standpoint because the theoreti-cal concept and research questions were formulated using existing theories that have been developed within the field under study. Hence, the deductive approach adopted for this study will ensure that the existing theories are used to assess the research questions. There are dif-ferent research approaches (see Figure 3.1) that can be utilized when relating theories with empirical data (Spens and Kovács, 2006) depending on the nature of the study.

Figure 3.1 Different research approaches (Spens and Kovács, 2006)

The deductive approach was appropriate for this study, so as to have a profound discussion with participants whom are fully involved in the manufacturing data and information han-dling, in pursuit to fulfill the purpose of the research.

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3.3.1 Qualitative data

This research has a qualitative approach based on the nature of the study, which is to get a deeper understanding of the current situation at the case company (i.e. the utilization of data and information in manufacturing preparation and assembly process). By adopting a qualita-tive research approach for this study, a holistic and comprehensive picture of the complexity of data management in manufacturing and assembly processes, which is characterized by mul-tiple sources of data, is developed (Recker, 2013).

3.3.2 Quantitative data

Although this research is fully qualitative in nature, however, numerical data was collected during direct observation and mouth-to-mouth conversation with operators and team leaders. The quantitative data was to determine the importance (with regards to scaling responses from the operators and engineers) of some qualitative data in the assembly instruction. Likert Scale was used to ascribe quantitative value to the qualitative data on the assembly work sheet, so as to determine which data is important and less important within the available data at the shop floor. Thus, numerical data was also collected in that regard.

3.4

Data collection method

When planning or conducting a case research, the first step according to Yin (2002) is to iden-tify the kind of method one should use in collecting the required data for the research. Data for the research was collected through literature review of previous research within the re-search area, observation using the traditional “Pencil and paper method”, and semi-structured interview with respondents so as to gain an insight of the studied phenomenon. This is in con-nection of the view of various authors, for example, (Bryman and Bell, 2007; Yin, 2009) of the different methods of collecting data according to the nature of the research. Knowledge is fundamental to any cause, and in order to gain the required knowledge of the specified prob-lem and answer the research questions, three methods were used. First, a literature review was conducted. Second, a direct observation, and third, an interview was carried out with the op-erators, production leaders, and the responsible production engineers within the area of inter-est. The reason for using the stated methods is based on triangulation – use and combination of different methods (e.g. direct observation, interview) in studying the same phenomenon, which according to Voss et al. (2002) is the underlying principle of data collection in case research. More so, the use of multiple sources of data on the same phenomenon increases data reliability (Voss et al., 2002).

3.4.1 Literature review

This research explore how data and information is handled in the manufacturing preparation and manual assembly operations, by investigating the available data and information to the operator and how the available data is actually utilized by the operators for carrying out as-sembly operations. Conducting an extensive literature review in the area of data and infor-mation management, particularly in manufacturing operations, was the first step taken in writ-ing this thesis. In order to find the appropriate article and books for this study, different

re-search database (e.g. Google Scholar, Lund University Library, and Chalmers University Li-brary) was used. Key words like data and information management, smart manufacturing, smart factory, manufacturing operations, were used as search criteria. This is in view to nar-row down the scope of the research topic. This was important in order to gather relevant theo-ries, concepts, and previous findings that were relevant to this research, and to in order to gain meaningful insight of the research area. Thus, literature review was the starting point of this thesis (Recker, 2013).

3.4.2 Observation

The next step was conducting a direct observation of assembly workflow at the shop floor in order to understand how data and information is used in assembly operations. The main rea-son behind the observation method is to know exactly what data and information is available within the shop floor, and to gain an insight (from the operator’s perspective) on what data and information is actually used for assembly operations. The observation was conducted by using the traditional pen and paper technique (which is basically having pencil and paper to document findings from observation and mouth-to-mouth conversation with operators, team leaders, etc.) to really capture primary (both qualitative and quantitative) data, which is avail-able within the assembly line. This was necessary so as to determine the difference between available data and actual used data by the operators, and as one of the basic requirements for this thesis (Yin, 2002; Bryman and Bell, 2007). The observation was not centered on any pro-cedures or framework as there is no guideline on how to conduct a direct observation in a case research, it was however conducted in such a way that it correlates with the answer the re-search question (Newcomer et al., 2015).

3.4.3 Semi-structured interview

As a result of the complexity of the research area, a semi-structured interview was performed with different respondents, for example, Production engineers, Technicians and introduction engineers. The main objective of conducting a semi-structured interview is to engage in a dis-cussion that could result to numerous questions and answer, rather than a direct answer (Saunders et al., 2007). Hence, interviewing several personnel in these areas ensured that re-quired data (relating to what data and information, processes and IT systems used in the prep-aration and creation of assembly instruction used by the operators at production sites) was gathered.

Several interviews were conducted with personnel from different plants within the organiza-tion. Personnel with a deep knowledge and insight within the manufacturing area of the or-ganization were interviewed. The reason for conducting at least two interviews from each plant is to have a different perspective of the research area. In addition, very few had a holistic understanding of the research area. Thus, by interviewing more than one personnel within the organization, a more detailed and holistic understanding of the problem was established (ku-zel, 1992). The interview was conducted conversationally face-to-face and through Skype (around the topic of research) with one respondent at a time, with a blend of closed- and open-ended questions. This was complemented by follow-up (e.g. “what”, “how”, “why”) questions through e-mail, which clearly describes the descriptive nature of this research, as well as the semi-structured interview (Newcomer et al., 2015). The interview was conducted in such a way that ethical issues (informed participation, confidentiality, anonymity, etc.) regarding research study, as provided by Bhattacherjee (2012) was observed. Questions were asked in

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such a manner that it does not pose a threat or discomfort to the respondents, so that the main purpose of conducting the interview was achieved. As stated by Yin (2003), conducting a case research interview requires maintaining concurrently two distinguishing levels at the same time, so as to fulfill the purpose of the interview and to ask questions that do not pose any form of inconveniences to the respondent.

3.5

Data analysis method

Two steps were adopted when analyzing the data that was collected during direct observation and semi-structured interview. This was performed simultaneously with the data collection because of the qualitative nature of the research, and to ensure that the collected data was or-ganize progressively, so as to obtain relevant information and arrive at a meaningful conclu-sion (Coffey and Atkinson, 1996).

The first step taken in acquiring primary data for this research was direct observation. Data collected through observation was voluminous; hence analysis of the data was required. One suitable method for performing an analysis in a qualitative case research is the data reduction method. Data reduction was preferred appropriate for this research because it is a continuous process of data analysis, which involves streamlining, abstracting, transforming and organiz-ing the data so as to reduce the collected data to the most relevant data for the research, thus, a final conclusion can be reached (Miles and Huberman, 1994).

The documentation and recordings form the semi-structured interview with the respondents was transcribed into text in order to ensure a detailed analysis and examination of the collect-ed data, determine the overall quality of the interview and how it relates with the research objectives (Kvale, 2007; Corbin and Strauss, 1990). Besides, the interview analysis was nec-essary to enable proper interpretation of the text within the context in which the interview was constructed (Bhattacherjee, 2012).

3.6

Research quality criteria

The purpose of conducting any research is to contribute to the body of science and increase knowledge about the particular area. Thus, the research quality must be measured. Heale and Twycross (2015) referred to this as scientific rigor – a concept most applied to qualitative research because of its non-experimental nature. Guba (1981) highlighted the following crite-ria for assessing reliability of a research.

• Credibility of the study – degree to which “confidence in the truth of the findings for

the subjects and the context in which the study was undertaken” has been established.

• Applicability – degree to which the research finding can be applied to other contexts

or settings.

• Consistency – an extent to which the research findings would be consistent if

investi-gation was repeated in same context.

3.6.1 Reliability

Reliability of a study is concerned with its consistency and credibility of the research findings (Kvale & Brinkmann, 2009) that clearly emphasizes that the same findings of the particular research should be attained when performed in the same context. Regardless of the number of times the study is repeated. This is performed in order to minimize any forms of error or bias-es in a case rbias-esearch (Yin, 2003). In order to evaluate the quality of this rbias-esearch and ensure that the study is consistent, multiple interviews were conducted (even with respondents with the same job title) so as to guarantee that the data collected was reliable and applicable for the proposed study (Bhattacherjee, 2012)

3.6.2 Validity

Validity in a qualitative research is concerned with the extent to which a measure characteriz-es the construct that is to be measured (Bhattacherjee, 2012). It emphasizcharacteriz-es the credibility of a research finding, and whether or not the collected data for the research measures what it is supposed to measure (Recker, 2013). The validity for the study was ensured by carefully re-viewing the interview questions with experts and supervisors in manufacturing engineering and technology development that have a holistic insight about the research area before the commencement of the data collection (Yin, 2009). Furthermore, the interview questions were also designed in such way that it was easy for the participants to grasp because if the inter-view questions were difficult to understand, responses and feedback might be incomplete, which can lead to bias of the findings. Conclusively, all interviews were recorded, as just tak-ing notes durtak-ing interview would not be enough, with regards to validattak-ing the collected data (Bhattacherjee, 2012; Recker, 2013).

3.6.3 Ethics

Ethical issues should be considered when conduction a research due to the unethical ways science has been manipulated and research activities been performed in a contrasting way to that of the norms of scientific conduct (Bhattacherjee, 2012). Ethics for the research was as-sured according to the guidelines of ethical principle (voluntary and informed participation, confidentiality and anonymity, disclosure, analysis and reporting) proposed by (Bhattacherjee, 2012).

• Voluntary and informed participation – all participants in this study were informed

about the intended purpose of the study, and were allowed to participate voluntarily. The interviewees were further informed in advance of the research purpose before the interview was planned, and immediately before the interview was conducted. A re-quest to record the conversation was also put forward.

• Confidentiality and anonymity – the participants were informed beforehand of the

anonymity of their names in the report; hence, the interview is totally anonymous. As it was necessary in order to protect their interest and future wellbeing, and keep their identity confidential.

• Disclosure – the research goal and purpose was thoroughly explained to the

partici-pants (interviewee) of the study, in order for them to know what the report plans to achieve, so they could decide whether or not to participate in the study.

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• Analysis and reporting – misrepresenting information and questionable claims

dur-ing the analysis were totally avoided, thus, the finddur-ings and results were reported, as they were uncovered.

3.7

Summary of the research method

The research methodology employed in writing and conducting this thesis is summarized in Figure 3.2 below. These methods were deemed suitable for this research because of the nature of the study. The author employed these methods (Table 3.1), so as to achieve the primary purpose of why the research was conducted.

4

Empirical data

A brief introduction of the case company Volvo GTO is presented in this chapter, followed by empirical data gathered through observation and interviews with different personnel within the Trucks and Powertrain organization.

The data for the analysis was gathered from two different organizations within one entity of the company. Due to the complexity of the manufacturing process and different operation involved, and in view to narrow down the scope of the observation process, a number of ac-tivities which characterizes heavy vehicle manufacturing and assembly where selected. These activities include Equipment Controlled Assembly, Media Routing and Clamping, Hole Pat-tern Recognition, Hidden Assembly, and Riveting. The data matrix for these activities, based on collected data and information from direct observation and interview is presented in Ap-pendix B1 B2, B3, B4, and B5, respectively. These activities were fundamental in selecting the stations where the direct observation technique was carried out.

In order to fully capture data and information used within these selected activities, assembly and pre-assembly working stations were considered suitable for this study. These stations also represents where the aforementioned activities is being carried out. A total of 13 assembly and pre-assembly station where selected across 3 production and assembly plants (which repre-sents manual assembly of complete vehicle, engine and transmission) within the case compa-ny. A total of 32 operators participated during the direct observation, where interaction with the operators was also made.

The investigation was planned and carefully carried out in all the 13 stations, so as to identify the data and information that is available for the operators, and how the operators actually use this information in real-time. The operators were also asked to rate the importance of the data and information available to them for assembly operations, using the Likert scale. The rating was from1 to 5, (1 being not important, 2 – slightly important, 3 – fairly important, 4 – im-portant, and 5 – very important).

Furthermore, a total of 10 different interviews were also conducted with production engineers/ technicians within the organizations, who are responsible for different activities in the manu-facturing process and the creation of assembly work instruction. In the same vein, the engi-neers/ technicians were asked to rate the importance of the data and information used for as-sembly operations. To further differentiate between the organizations, the organizations will be represented by production plant A, B, and C, respectively.

4.1

Company profile

Group Trucks Operations (GTO) is the truck industrial entity within the Volvo Group. Volvo Group comprises of ten business areas, i.e. Volvo Trucks, UD Trucks, Renault Trucks, Mack Trucks, Group Trucks Asia and JVs, Volvo Construction Equipment, Volvo Buses, Volvo Penta, Governmental Sales and Volvo financial service solutions. The group is one of the

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world’s leading manufacturers of trucks, buses, construction equipment, and marine and in-dustrial engines. The group has approximately 100,000 employees, with manufacturing and assembly plants in more than 20 countries worldwide.

Volvo GTO is responsible for truck manufacturing, which includes Cab and Vehicle assem-bly, powertrain production (Engines and Transmission), Logistics services, Parts distribution and remanufacturing. GTO has a global industrial footprint and manufactures state-of-the-art products of the brands off the Volvo Group, with 43 plants and 55 distribution centers within the organization. The organization also includes approximately 30,000 employees in 33 coun-tries.

Cab & Vehicle assembly

GTO Cab and Vehicle Assembly Cab over engine (Trucks) is responsible for the manufactur-ing and assembly of cabs, complete trucks and knock-down kits for the Volvo, Renault and UD brands in Europe, Middle east and Africa. One of largest plant located in Tuve (Gothen-burg) is responsible for manufacturing and assembly of complete heavy-duty trucks and pro-vides overseas material. There are approximately 2000 employees in Tuve plant.

Powertrain production

GTO Powertrain production is one of the world’s largest manufacturing of heavy-duty en-gines for commercial vehicles, and provides diesel enen-gines and transmission systems to the Volvo Group business areas. Powertrain production also remanufactures and renovates a range of products, and has facilities in Skövde, Köping, amongst others.

The facility in Skövde produces and supplies diesel engines and components to the Volvo Group. The assembly line is one of the world’s largest diesels producing units’ segments 9-18liters, i.e. 115,000 engines production capacity annually. The facility has approximately 2800 employees. The facility in Köping produces transmission systems – gearbox (e.g. I-shift and Powertronic) and marine drives, within the Volvo Group. The facility has approximately 1,450 employees.

4.2

Volvo GTO Information systems

The Group Truck Operations utilizes different IT systems to carry out its manufacturing oper-ations. These systems are either developed in-house or from different IT suppliers. Some of the systems that is relevant for this study and used for the manufacturing preparation (Engi-neering) and manufacturing execution (assembly) processes are described below.

KOLA - derived from “KOnstruktionsdata LAstvagnar”, which stands for Design data truck, is a Product Data Management (PDM) system, which is developed in-house by Volvo IT, and used by Volvo Group, mainly within product development to document product offering and design solution. It is a product development tool that has been designed to provide the engi-neering department with the ability to see their design solutions from a common perspective. KOLA is the core system that holds or houses all information about trucks with reference to and relation between parts and digital models. The information in KOLA is organized in

sev-eral structures, which together makes the whole system. KOLA is made up of logic structures, such as item structure, variant structure, and item to variant structure.

SPRINT - a manufacturing preparation system, which is developed in-house by Volvo IT, contains almost all information relating to the production of the vehicle. SPRINT is a tool to deliver a quality assured production structure to the assembly process. Its essential function is to ensure accurate and precise material is at the preferred place, and in the right time, together with the accompanying assembly instruction. It is used for the manufacturing preparation (be-fore the product reaches serial production) to keep track of the flow of material, tool data, structure of the factory, operator structure and instructions. All production data (except the BoM, which is from KOLA system), is entered manually into the SPRINT system by the pro-duction engineer during the latter stage of the product development process. The data in the SPRINT system is kept up-to-date during the entire product lifecycle.

BEMS – derived from “BEredning Monterings Säkring”, is a manufacturing preparation sys-tem used by production engineers to prepare assembly instruction for Assembly Assurance System (AAS). Its features among other, includes, sets of operation sequence (BoM sorting), assembly instruction to product specification connection, manual input of text and illustration, real-time update of instruction and resending, equipment preparation and linkage to the opera-tion, etc.

MONT.AAS - Assembly Assurance System is an assembly instruction display system used by the operators to control or execute assembly operations. It controls the shop floor equip-ment and guides the operators through every steps of the assembly process. It features, among others, real-time instruction for operators, assembly tools ordering and activation, and dis-playing pictures and icons for better visualization.

GM-FMD - Global Manufacturing Master Data is an application used by Cab and Vehicle organization of the Volvo Group Trucks Operation to gather data from a number of applica-tions in order to create a Part Master for the manufacturing preparation of a heavy vehicle. When the Part Master is created, it is passed onto other application that needs the Part Master information to carry out their operations.

MOMS – derived from “Material och Monterings Styrning”, is a product preparation man-agement system (developed in-house by Volvo IT), which is used for the manufacturing prep-aration of assembly instruction.

4.3

Process and preparation of assembly instruction

According to the conducted interview, the preparation and process leading to assembly in-struction begins with customer’s order. This is often (if not typically) characterized by three elements, which is customer requirements, product improvements, and quality requirements. Depending of the three elements, the process begins with the design of the components ac-cording to specifications and requirements, together with the possible assembly ergonomics of the product. When the design is completed, a DCN is created. DCN (Design Change Note), also referred to as ECN (Engineering Change Notice) is a document which contains the vali-dation for changes that is made to a system or component after the original design of the

com-– 34 com-–

ponent is finalized. It authorizes the changes to a specific design throughout the prototype and lifecycle phases of a product.

Once the DCN or ECN is created, all proposals are reviewed to ensure that it fulfills the set requirements and its suitability for manufacturing. Depending on if the DCN review is flagged satisfactory or unsatisfactory, a detailed time setting and analysis (e.g. process time of a specific operation) based on CAD files or modules is performed. Otherwise, if the review is unsatisfactory the design change or request is performed through a system or application known as PROTUS. The PROTUS system is a tool that allows test prototype specification, support prototype building and report assembly deviations, support prototype testing, and re-port problems linked with the production or manufacturing process. This is done in order to report or adjustment the requested design change. This is followed by balancing (determining where certain components is suitable on the production line), station marking, which is basi-cally performing station readiness and assigning components with regards to station and oper-ator. These activities leads to or facilitates the creation of preparation of the assembly instruc-tion in SPRINT – a manufacturing preparainstruc-tion system or the BEMS – a manufacturing prepa-ration system (depending of the organization, e.g. Cab & Vehicle assembly or Powertrain production), which is then presented in different systems (SPRINT AI and MONT AAS) to the operators for assembly operations at the shop floor. Three main systems/ application i.e. PROTUS, KOLA and SPRINT are used by the engineers (production engineer/ Technical preparation engineer) to ensure that information for assembly instruction is accurate and pre-cise, in Cab & Vehicle assembly production plant.